Sustainable Iot and Data Analytics Enabled Machine Learning Techniques and Applications

ISBN 10: 9819753643 / ISBN 13: 9789819753642

Verlag: Springer, 2024

Beschreibung

2024th edition NO-PA16APR2015-KAP.

Bestandsnummer des Verkäufers 26401278321

Bibliografische Details

• Titel: Sustainable Iot and Data Analytics Enabled Machine Learning Techniques and Applications
• Verlag: Springer
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 9819753643
• ISBN-13: 9789819753642
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

This book provides a structured presentation of machine learning related to vision, speech, and natural language processing. It addresses the tools, techniques, and challenges of machine learning algorithm implementation, computation time, and the complexity of reasoning and modeling of different types of data. The book covers diverse topics such as semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, natural language processing, traffic and signaling, driverless driving, and radiology. The majority of smart applications have a need for a sustainable Internet of things (IoT) and artificial intelligence. Active research trends and future directions of machine learning under big data analytics are also discussed. Machine learning is a class of artificial neural networks that have become dominant in various computer vision tasks, attracting interest across a variety of domains as they are a type of deep neural networks efficient in extracting meaningful information from visual imagery.

Über die Autorin bzw. den Autor

Dr. Ajantha Devi Vairamani is the research head at AP3 Solutions in Chennai, India. She earned her Ph.D. from the University of Madras in 2015 and has since made significant contributions to the fields of computer science and artificial intelligence. She has been involved in numerous UGC Major Research Projects and holds prestigious certifications such as Microsoft Certified Application Developer (MCAD), Microsoft Certified Technology Specialist (MCTS), and Certified Artificial Intelligence Engineer (CAIE™). With over 50 published papers in international journals and conferences, she is also an accomplished author and editor in computer science. She actively participates in international conferences and serves on various committees, contributing to research collaboration and advancement. Her pioneering work in AI, machine learning, and deep learning has resulted in Australian patents and numerous awards. Her research spans image processing, signal processing, pattern matching, and natural language processing, addressing real-world challenges with innovative solutions.